This Azure Machine Learning hands-on course is designed for beginners who want to build practical machine learning skills using Microsoft Azure. The course focuses on real-world workflows, guiding learners from account setup to building and training machine learning models.
Students will begin by creating an Azure for Students account and setting up an Azure Machine Learning workspace. The course then introduces compute instances and explains how to prepare the environment for running machine learning experiments in the cloud.
A major focus of the course is data preparation, including importing data from SQL Server, summarizing datasets, selecting relevant features, and reducing unnecessary data. Learners will also study data cleaning techniques such as handling missing values and addressing outliers using clip values.
The course then moves into building predictive models, focusing on regression techniques such as linear regression for insurance datasets and working with categorical features. Learners will also explore feature selection methods, including filter-based feature selection to improve model accuracy and performance.
In addition, students will learn how to build and run Azure Machine Learning pipelines, allowing them to automate workflows and organize machine learning processes efficiently.
By the end of the course, learners will be able to confidently prepare data, build regression models, and create automated machine learning pipelines using Azure ML.
